MOSAIC: Modular Scalable Autonomy for Intelligent Coordination of Heterogeneous Robotic Teams
🤖 AI Summary
This work addresses the limitations of multi-robot deployment in harsh environments—particularly those stemming from reliance on manual teleoperation, which incurs constrained scalability and communication delays. To overcome these challenges, the authors propose a multi-layered autonomous architecture grounded in a unified task abstraction based on points of interest (POIs). This framework integrates modular and scalable coordination mechanisms that combine team redundancy with capability specialization, enabling a single operator to efficiently supervise and dynamically allocate tasks among a heterogeneous robot team. Experimental validation in a lunar-analog exploration scenario demonstrates that a five-robot team achieves 82.3% mission completion even when one robot fully fails, attaining an autonomy level of 86% while reducing operator workload to 78.2%, thereby significantly enhancing system robustness and scalability.
Application Category
📝 Abstract
Mobile robots have become indispensable for exploring hostile environments, such as in space or disaster relief scenarios, but often remain limited to teleoperation by a human operator. This restricts the deployment scale and requires near-continuous low-latency communication between the operator and the robot. We present MOSAIC: a scalable autonomy framework for multi-robot scientific exploration using a unified mission abstraction based on Points of Interest (POIs) and multiple layers of autonomy, enabling supervision by a single operator. The framework dynamically allocates exploration and measurement tasks based on each robot's capabilities, leveraging team-level redundancy and specialization to enable continuous operation. We validated the framework in a space-analog field experiment emulating a lunar prospecting scenario, involving a heterogeneous team of five robots and a single operator. Despite the complete failure of one robot during the mission, the team completed 82.3% of assigned tasks at an Autonomy Ratio of 86%, while the operator workload remained at only 78.2%. These results demonstrate that the proposed framework enables robust, scalable multi-robot scientific exploration with limited operator intervention. We further derive practical lessons learned in robot interoperability, networking architecture, team composition, and operator workload management to inform future multi-robot exploration missions.
Problem
Research questions and friction points this paper is trying to address.
multi-robot coordination
autonomous exploration
heterogeneous robotic teams
operator workload
scalable autonomy
Innovation
Methods, ideas, or system contributions that make the work stand out.
Modular Autonomy
Heterogeneous Robot Teams
Points of Interest (POI)
Scalable Multi-Robot Coordination
Operator Workload Management
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